Skip to main content


Research Interests

  • Computational Biology and Bioinformatics
    • Cancer Transcriptome
    • Biomarker Identification
    • Post-transcriptional Regulation
    • Drug Sensitivity Prediction
  • Machine Learning
    • Network-based Learning
    • Semi-supervised Learning
    • Reinforcement Learning
    • Transfer Learning

Other Experience

  • Research Associate, University of Minnesota-Twin Cities (2015-2017)
  • Research Intern, Takeda Pharmaceuticals Company (2014)

Professional Activities

  • ACM-BCB 2019 Workshop Chair
  • Program Committee Member: ICDM 2018, 2019, ACM-BCB 2018, 2019, ICCABS 2017
  • NSF panel member (2018)
  • Reviewers for Nucleic Acids Research, Bioinformatics, PLoS One, BMC Bioinformatics, BMC Genomics, and others

Honors & Awards

  • NSF CRII (2018)
  • Best Poster Award, The 6th Annual Biomedical Informatics and Computational Biology Research Symposium (2014)

Selected Publications

  • Jae-Woong Chang*, Wei Zhang*, Hsin-Sung Yeh, et al. An Integrative Model for Alternative Polyadenylation, IntMAP, Delineates mTOR-modulated Endoplasmic Reticulum Stress Response. Nucleic Acids Research, 2018.
  • Wei Zhang, Jeremy Chien, Jeongsik Yong, and Rui Kuang. Network-based Machine Learning and Graph Algorithms for Precision Oncology. npj Precision Oncology, 2017.
  • Jae-Woong Chang*, Wei Zhang*, Hsin-Sung Yeh, et al. mRNA 3’UTR Shortening is a Molecular Signature of mTORC1 Activation. Nature Communications, 2015.
  • Wei Zhang, Takayo Ota, et al. Network-based Survival Analysis Reveals Subnetwork Signatures for Predicting Outcomes of Ovarian Cancer Treatment. PLoS Comput Biol, 2013.